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Tukino

Universitas Buana Perjuangan Karawang

Author-ID : 5670920

Religion Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Analisis Sentimen, Text Mining Penerapan Analisis Sentimen Dan Naive Bayes Terhadap Opini Penggunaan Kendaraan Listrik Di Twitter Adittia Agustian; Tukino; Fitria Nurapriani
Jurnal Tika Vol 7 No 3 (2022): Jurnal Teknik Informatika Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh

Twitter is the most popular social media today. Can find out various Twitter responses that fall into the positive, neutral, or negative categories. Technological advances at this time are so rapid that vehicles will provide fuel for electric power or are called electric vehicles. Indonesia has become a country that encourages acceleration in the use of electric vehicles, according to the Minister of State-Owned Enterprises circular letter. The advancement of electric-powered vehicles is an innovation and technology that will continue to develop and transform. With the presence of the electric vehicle, the Indonesian government will serve as an important guest vehicle at the G20 Summit activities in Bali, Indonesia. The purpose of this study is to determine the public's response to electric vehicles which are currently widely used among the people of Indonesia. To find out the public response, sentiment analysis is needed through the responses of Twitter users. By generating positive, neutral, or negative categories. Based on the results of the classification of sentiment analysis on the support of electric vehicles. Data collection uses the Twitter API as an open source that can retrieve Twitter user responses, then the data cleaning process is carried out, converting Indonesian to English, then tested using the Naïve Bayes algorithm, and visualizing twitter data using python. Based on the classification results, public response to electric vehicles is more positive with 82% precision and 44% recall. By having 80% data accuracy through the Naive Bayes confusion matrix through the text mining process, python text blob, and word cloud as the relationship between words and twitter text

Klasifikasi Hasil Penjualan Minuman Ringan Pada Koperasi Berdasarkan Jenis Barang Menggunakan Algoritma K-Means Clustering Awaljan Situmorang; Tukino Tukino; Elfina Novalia; Sandi Ahmad
Jurnal Tika Vol 7 No 3 (2022): Jurnal Teknik Informatika Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh

The joint cooperative store is one of the efforts given by the joint cooperative management to increase cooperative income by calculating profits every year and distributing them to cooperative members in the form of money, commonly known as SHU or the remaining results of operations. However, there are still shortcomings in the implementation of cooperative sales management, one of which is the sale of soft drinks. There are still errors in determining the high and low volume of beverage sales. This research will help cooperative managers to categorize beverage sales data so that customer demand for soft drinks can be fulfilled properly. The data collected from January 2020 to September 2022 is the sale of 11,945 drinks from 15 soft drinks at the Koperasi Bersama store. This research aims to group the sales recapitulation results into a cluster using a data mining approach using the K-Means clustering algorithm. Grouping sales data according to its characteristics. The results of this study indicate that 1 soft drink is included in cluster 0 which is classified as high sales volume, while 14 soft drinks are included in cluster 1 which is classified as low sales volume.

Clustering User Sentiment Transportasi Online Gojek Dan Grab Dengan Metode K-Means Dyno Syaiful Annam; Agustia Hananto; Fitria Nurapriani; Tukino Tukino
Jurnal Tika Vol 8 No 2 (2023): Jurnal Teknik Informatika Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/tika.v8i2.2165

As an online transportation service, people often discuss it by sharing their opinions through various social media platforms, one of which is Google Play reviews. The opinions given by the public regarding online transportation services are diverse. Users provide reviews about the application, and naturally, users will choose an application with good reviews. However, monitoring the opinions of the general public is not easy, given the large volume of data to be processed. Therefore, the researcher aims to obtain accurate and precise information from user reviews of Gojek and Grab using clustering techniques, specifically the K-means method, using the RapidMiner application. The results of the testing of both applications can be summarized as follows: Gojek and Grab receive reviews that are not significantly different, although Grab's reviews are slightly better. The classification using the K-Means method offers a solution to the issue of sentiment analysis in user reviews of online transportation applications.

Co-Authors ., Widiyawati Aditia Agustian Adittia Agustian Agneresa Agneresa Ammar Sufyan April Lia Hananto Aprilia Putri Nardilasari Aurel Adhitya Anwar Awaljan Situmorang Baenil Huda Danny Manongga Daryanto Herdiana Dyno Syaiful Annam Faqih Pratama Muthi Fitria Nurapriani Fitria Nurapriani Fizra Firdaus Nillan Gunawan Gunawan Hananto, Agustia Insan Kamaludin Arifin Irpan Hilmi Kamila Berkah* Muhamad Djaka Permana Muhamad Rizky Arfani Muhammad Hatami Muhammad Khaerudin Novalia, Elfina Novia Cahya Utami Priyatna, Bayu Riandi Nandaputra Ruliansyah Ruliansyah Saepul Aripiyanto Saepul Aripiyanto Sandi Ahmad Seia Piantara Shofa Shofia Hilabi Shofa Shofiah Hilabi Siti Masruroh Sutarto Sutarto Tatuhey, Emy Lenora Tita Puspita Sari Witulas Ambang Cahyati Wresti Andriani

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